Slides begin

• (we assume successful installation, R fluency)
• (we emphasize pictures / code over longwinded text / bullets)

Rough ToC

30 minutes of presentation

• Why are we building this?
• Data formats (why these? who cares about versions?)
• Signal processing operations: Give a bunch of examples, but focus on 2 (perhaps “latency/reporting investigation” and “outlier detection+correction”)
• Forecasting in practice (easily incorporate new data sources, forecasts of many horizons, many jurisdictions, ease of QC, compare to baselines, backtesting)
• Simple AR-type Forecasters and Classifiers

Rough ToC (continued)

30 minutes of exercise

• Take data from 2-3 sources available across jurisdictions
• Investigate leadingness of some signals
• Implement a forecaster:
  • Normalize by population,
  • other preprocessing? (perhaps 7dav, sensorization?)
  • choose some lags to use
  • Compare a couple of engines
  • visualize the output

Callouts

Some code with highlights

rf <- arx_forecaster(
  epi_data = jhu, 
  outcome = "death_rate", 
  predictors = c("case_rate", "death_rate", "fb-survey"),
  trainer = parsnip::rand_forest(mode = "regression"), # use ranger
  args_list = arx_args_list(
    ahead = 14, # 2-week horizon
    lags = list(c(0:4, 7, 14), c(0, 7, 14), c(0:7, 14)), # bunch of lags
    levels = c(0.01, 0.025, 1:19/20, 0.975, 0.99), # 23 ForecastHub quantiles
    quantile_by_key = "geo_value" # vary q-forecasts by location
  )
)